Last updated: 2021-02-08

Checks: 7 0

Knit directory: Supplemenetary_data_files_automation_v2/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201119)

The command set.seed(20201119) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 555b1a2

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 555b1a2. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    code/Construction/
    Ignored:    data/Inputs/Large_tables/

Untracked files:
    Untracked:  DELETE.jpeg
    Untracked:  Rplot.jpeg
    Untracked:  analysis/graphing_heierachies.rmd
    Untracked:  delete/

Unstaged changes:
    Modified:   analysis/SUT_presentation_RY2018.Rmd
    Modified:   analysis/cleaning_SEPH.Rmd
    Modified:   analysis/supp_files.rmd
    Modified:   analysis/technical_R_tips.Rmd
    Modified:   code/Publishing_script.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Provincial_view.rmd) and HTML (docs/Provincial_view.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 555b1a2 arman 2021-02-08 wflow_publish(c(“analysis/Provincial_view.rmd”, “analysis/index.Rmd”))
html acae2de arman 2021-02-08 Build site.
Rmd 5b9cbf6 arman 2021-02-08 fixed the date
html c978ec7 arman 2021-02-08 Build site.
html 210b49e arman 2021-02-08 Build site.
Rmd 39b2d18 arman 2021-02-08 wflow_publish(c(“analysis/Provincial_view.rmd”, “analysis/index.Rmd”))
Rmd 05f2e30 arman 2021-02-08 Finished LFS
Rmd 03e788f arman 2021-02-07 fixed the employment
Rmd 07cced5 arman 2021-02-05 added employment by industury
html 3077a6a arman 2021-02-04 Build site.
Rmd 3a7a11e arman 2021-02-04 wflow_publish(c(“analysis/Provincial_view.rmd”, “analysis/index.Rmd”))
html 0276906 arman 2021-02-04 Build site.
Rmd 8fdc2eb arman 2021-02-04 added CPI with goods services and all items
Rmd 41617a0 arman 2021-02-04 ADDED the tables for prices/agriculture
html f449255 arman 2021-02-03 Build site.
Rmd 247d0fd arman 2021-02-03 wflow_publish(c(“analysis/Provincial_view.rmd”, “analysis/index.Rmd”))
Rmd 3db0059 arman 2021-02-03 added back the provinces to filter construction on; created and used across to add _K to all the columns and also to create summary annual tables easier
Rmd 88a4612 arman 2021-02-02 added a simple version of construction; will need some refinement. construction is rather complicated
Rmd a105189 arman 2021-02-01 addded Immigration and a method to cacluate any tibbles first difference and percent change
html 6a066ff arman 2021-01-29 Build site.
html ad426b3 arman 2021-01-29 Build site.
Rmd aa8d76b arman 2021-01-29 wflow_publish(c(“analysis/Provincial_view.rmd”, “analysis/index.Rmd”))
Rmd ab73d32 arman 2021-01-29 Completed the master table for Trade population and PGDP
Rmd 777e601 arman 2021-01-28 created an annualized trade table.
Rmd dcf20f4 arman 2021-01-27 finished monthly trade with YOY % change.
Rmd 974fcc7 arman 2021-01-26 now measuring year over year average;
Rmd 3deba0d arman 2021-01-26 NDM has no consistent way of displaying dates; each table needs to be parsed in a custom manner. in this commit I use the parse date function in tidyverse to create a custom parse function for the dates in the trade table. hopefully I can use this to parse more
html ffc28e9 arman 2021-01-22 Build site.
Rmd d13d74f arman 2021-01-22 scraped the params and replaced a piece of r code; got the left join to work and merged the PGDP and population table
Rmd 704ee1d arman 2021-01-21 fuxed the developer_mode
Rmd 76913f8 arman 2021-01-21 testing new creds
Rmd aaaadf9 arman 2021-01-21 set up the first table. it all works
Rmd 2ae6621 arman 2021-01-20 added tables and the developer mode for including stuff in the document
Rmd edeeade arman 2021-01-19 ported the fucntions and set up added code lists and functions to be used for the project.
html ac1cbff arman 2021-01-19 Build site.
html 1771361 arman 2021-01-19 Build site.
html 49fbbde arman 2021-01-19 Build site.
Rmd 8538afc arman 2021-01-19 starting the official provincial view

Introduction

in this document I will create a view based on the following tables

  • contribution to total economy

  • population 17-10-0009-01.

  • immigration 17-10-0008-01.

  • labor force Statistics Canada, table 14-10-0327-01.

  • Farms, by operation type 32-10-0403-01.

  • Aquaculture in Canada 32-10-0107-01.

  • Manufacturing industries 16-10-0117-01.

  • International merchandise trade by province, commodity, and Principal Trading Partners 12-10-0119-01

  • Investment in Building Construction(monthly) 34-10-0175-01

  • Building permits, by type of structure and type of work 34-10-0066-01

set up

in this section I will set up the foundation for the program; it is broke up into 3 parts; package dependency and path; functions,and finally code lists.

Package dependency

in this section I will manage dependencies ( the packages used in this project; in R you need to state what you will use (kinda) and here we will simply state them) these will have to be installed for the program to run properly. if you run this in the cloud the commands below should be sufficient. if you uncomment the first 5 lines

paths

all the files in this program will now be created relative to where you run this script; it will create all the necessary subflorder and files from the location below

here()
[1] "C:/Users/Arman/Documents/Statcan/R_projects/Supplemenetary_data_files_automation_v2"

codelists

stating what the code lists are for the project to sort and query data from; if you want to add some thing for the table to include you can simple add it here and it should work;. it is designed this way to make this program more extendable, and maintainable. the added complexity is worth it

table numbers

if you have any tables to add please add it here; it will make it easier to maintain this program

functions

here I will describe the functions required in the project. the most important one is the one for creating interactive tables in the browser that allow you to change which columns to show and to search through tables.

it is important for the search function and performance for the columns to be categories and/or numerics.

TODO: find a way to handle sig dig

importing/cleaning the tables.

i will attempt to import and clean all the tables ### PGDP here I will be working with Provincial GDP

2 digit naics

pgdp_table_number <- "36-10-0402"
pgdp <- cansim::get_cansim_ndm(pgdp_table_number)
Accessing CANSIM NDM product 36-10-0402 from Statistics Canada
Parsing data
Folding in metadata
pgdp <- janitor::clean_names(pgdp)
pgdp %>%
  select(ref_date, geo, north_american_industry_classification_system_naics, value, hierarchy_for_north_american_industry_classification_system_naics, coordinate, classification_code_for_north_american_industry_classification_system_naics, value_2) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>%
  rename(CPC = value_2) %>%
  filter(ref_date > from_this_year) %>%
  filter(value == "Contributions to percent change") %>%
  select(!value) %>%
  filter(classification_code_for_north_american_industry_classification_system_naics %in% c(two_digit_naics_code)) %>%
  group_by(ref_date, north_american_industry_classification_system_naics, geo) -> clean_PGDP
# maybe wait till the end to convert the tables to save some space

interactive_table(clean_PGDP)
# data.table::fwrite(here("output", str_glue("Table_{tablenumber}_CPI_by_province_product_groups_{params$from_this_year}-{params$to_this_year}")))

provincial PGDP

provincial GDP for master table

pgdp
# A tibble: 302,289 x 21
   ref_date geo   dguid value north_american_~ uom   uom_id scalar_factor
   <chr>    <chr> <chr> <chr> <chr>            <chr> <chr>  <chr>        
 1 1997     Newf~ 2016~ Curr~ All industries ~ Doll~ 81     millions     
 2 1997     Newf~ 2016~ Curr~ Goods-producing~ Doll~ 81     millions     
 3 1997     Newf~ 2016~ Curr~ Service-produci~ Doll~ 81     millions     
 4 1997     Newf~ 2016~ Curr~ Industrial prod~ Doll~ 81     millions     
 5 1997     Newf~ 2016~ Curr~ Non-durable man~ Doll~ 81     millions     
 6 1997     Newf~ 2016~ Curr~ Durable manufac~ Doll~ 81     millions     
 7 1997     Newf~ 2016~ Curr~ Information and~ Doll~ 81     millions     
 8 1997     Newf~ 2016~ Curr~ Information and~ Doll~ 81     millions     
 9 1997     Newf~ 2016~ Curr~ Information and~ Doll~ 81     millions     
10 1997     Newf~ 2016~ Curr~ Energy sector [~ Doll~ 81     millions     
# ... with 302,279 more rows, and 13 more variables: scalar_id <chr>,
#   vector <chr>, coordinate <chr>, value_2 <dbl>, status <chr>, symbol <chr>,
#   terminated <chr>, decimals <chr>, geo_uid <chr>,
#   classification_code_for_value <chr>, hierarchy_for_value <chr>,
#   classification_code_for_north_american_industry_classification_system_naics <chr>,
#   hierarchy_for_north_american_industry_classification_system_naics <chr>
pgdp %>%
  select(ref_date, geo, north_american_industry_classification_system_naics, value, value_2) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>%
  rename(CPC = value_2) %>%
  filter(ref_date > from_this_year) %>%
  filter(value == "Contributions to percent change") %>%
  select(!value) %>%
  filter(north_american_industry_classification_system_naics == "All industries [T001]") %>%
  pivot_wider(names_from = north_american_industry_classification_system_naics, values_from = CPC) %>%
  janitor::clean_names() %>%
  rename(CPC_All_industries = all_industries_t001) -> master_pgdp

population

library(summarytools)
Registered S3 method overwritten by 'pryr':
  method      from
  print.bytes Rcpp
For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')

Attaching package: 'summarytools'
The following object is masked from 'package:tibble':

    view
population <- cansim::get_cansim_ndm(population_table_number)
Accessing CANSIM NDM product 17-10-0005 from Statistics Canada
Parsing data
Folding in metadata
population %>%
  janitor::clean_names() %>%
  select(1, 2, 4, 5, 12) %>%
  rename(population = value) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>%
  filter(ref_date > from_this_year) %>%
  filter(age_group == "All ages") %>%
  filter(sex == "Both sexes") %>%
  select(1, 2, 5) -> clean_population
# view(dfSummary(clean_population))
interactive_table(clean_population)

Trade monthly

This is the YOY percentage change difference for the monthly trade by NAPC I will create the annual series tomorrow. simply sum it over the year. it seems to be what the chief economist did and also it makes sense since it is already seasonally adjusted

trade <- cansim::get_cansim_ndm(trade_table_number)
Accessing CANSIM NDM product 12-10-0119 from Statistics Canada
Parsing data
Folding in metadata
trade <- janitor::clean_names(trade)
trade %>%
  select(ref_date, geo, trade, north_american_product_classification_system_napcs, principal_trading_partners, value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y-%m")) %>%
  filter(ref_date > from_this_year) %>%
  pivot_wider(names_from = trade, values_from = value) %>%
  janitor::clean_names() %>%
  arrange(north_american_product_classification_system_napcs, principal_trading_partners, geo, ref_date) %>%
  group_by(north_american_product_classification_system_napcs, principal_trading_partners, geo) %>%
  mutate(yoy_pct_change_import = ((import / lag(import, n = 12, order_by = ref_date)) * 100) - 100) %>%
  mutate(yoy_first_dif_import = (import - lag(import, n = 12, order_by = ref_date))) %>%
  mutate(yoy_pct_change_export = ((domestic_export / lag(domestic_export, n = 12, order_by = ref_date)) * 100) - 100) %>%
  mutate(yoy_first_dif_export = (domestic_export - lag(domestic_export, n = 12, order_by = ref_date))) %>% interactive_table()
Warning in instance$preRenderHook(instance): It seems your data is too big
for client-side DataTables. You may consider server-side processing: https://
rstudio.github.io/DT/server.html
  # group_by(3,4,2) %>%
  
#data.table::fwrite(here::here("delete", str_glue("Table_{trade_table_number}_Trade_full_table_from{from_this_year}.csv")))

Trade annual

annualized trade; run this in mid feb to make sure all the months are there for a reference year. doing so earlier will result in the most recent year being underepesented the best choice is to use moving average or an estimated value below is the raw trade data from NDM; it comes monthly and with a lot of data I filter out most of it for this provincial view

# make sure you run this after the december release; 8 weeks after reference period is the release
library(tidyverse)
library(tsibble)
library(lubridate)

Attaching package: 'lubridate'
The following object is masked from 'package:tsibble':

    interval
The following objects are masked from 'package:base':

    date, intersect, setdiff, union
trade <- cansim::get_cansim_ndm(trade_table_number)
Reading CANSIM NDM product 12-10-0119 from cache.
trade <- janitor::clean_names(trade)
trade %>%
  select(ref_date, geo, trade, north_american_product_classification_system_napcs, principal_trading_partners, value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y-%m")) %>%
  filter(ref_date > from_this_year) %>%
  pivot_wider(names_from = trade, values_from = value) %>%
  janitor::clean_names() -> clean_trade
data_tsbl <- as_tsibble(clean_trade, key = c(north_american_product_classification_system_napcs, principal_trading_partners, geo))
Using `ref_date` as index variable.
data_tsbl %>%
  group_by_key() %>%
  index_by(year_month = ~ year(.)) %>%
  summarise(
    import = sum(import, na.rm = TRUE),
    export = sum(domestic_export, na.rm = TRUE)
  ) -> annual_trade
# saving the CSV {} between these things the program will put in R code
annual_trade %>% interactive_table()
Warning in instance$preRenderHook(instance): It seems your data is too big
for client-side DataTables. You may consider server-side processing: https://
rstudio.github.io/DT/server.html
#data.table::fwrite(here::here("delete", str_glue("Table_{trade_table_number}_Trade_annual{from_this_year}.csv")))

annual trade filtered and left joined into master table

as_tibble(annual_trade) %>%
  filter(principal_trading_partners == "All countries") %>%
  filter(north_american_product_classification_system_napcs == "Total of all merchandise") %>%
  rename(ref_date = year_month) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>%
  select(geo, ref_date, import, export) -> master_trade
left_join(clean_population, master_trade) %>% left_join(master_pgdp) -> master_provincial_table
Joining, by = c("ref_date", "geo")
Joining, by = c("ref_date", "geo")

Immigration table

Immigration <- cansim::get_cansim_ndm(immigration_table_number)
Accessing CANSIM NDM product 17-10-0008 from Statistics Canada
Parsing data
Folding in metadata
Immigration <- janitor::clean_names(Immigration)
Immigration %>%
  select(ref_date, geo, components_of_population_growth, value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y /%Y")) %>%
  filter(ref_date > from_this_year) %>%
  pivot_wider(names_from = components_of_population_growth, values_from = value) %>%
  group_by(geo) %>%
  clean_names() -> clean_immigration
master_provincial_table %>%
  left_join(clean_immigration) -> master_provincial_table
Joining, by = c("ref_date", "geo")

construction

all construction numbers are for total type of work and are in unadjusted K dollar mabye I should just use 23A_K_ind

construction <- cansim::get_cansim_ndm(building_construction_table_number)
Accessing CANSIM NDM product 34-10-0175 from Statistics Canada
Parsing data
Folding in metadata
construction <- janitor::clean_names(construction)
construction %>%
  select(ref_date, geo, type_of_structure, type_of_work, investment_value, value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y-%m")) %>%
  filter(ref_date > from_this_year) %>%
  filter(geo %in% provinces) %>%
  filter(type_of_work == "Types of work, total") %>%
  select(!type_of_work) %>%
  filter(investment_value %in% c("Unadjusted - constant")) %>%
  select(!investment_value) %>%
  filter(type_of_structure %in% c("Total residential and non-residential", "Total residential", "Total non-residential")) %>%
  pivot_wider(names_from = type_of_structure, values_from = value) %>%
  clean_names() %>%
  rename_if(is.numeric, funs(str_c(., "_investment_unadjusted_K$"))) -> clean_construction
Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
data_tsbl <- as_tsibble(clean_construction, key = c(geo))
Using `ref_date` as index variable.
data_tsbl %>%
  group_by_key() %>%
  index_by(year_month = ~ year(.)) %>%
  summarise(across(where(is.numeric),list( a = sum))) -> master_construction
master_construction %>%
  as_tibble() %>% rename(ref_date = year_month) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>%
  select(everything()) -> master_construction

master_provincial_table <- left_join(master_construction,master_provincial_table)
Joining, by = c("geo", "ref_date")
# saving the CSV {} between the

these other two tables that I am not sure if I will include

Prices

here in this section I will ad prices

CPI

cpi <- cansim::get_cansim_ndm(cpi_table_number)
Accessing CANSIM NDM product 18-10-0005 from Statistics Canada
Parsing data
Folding in metadata
cpi <- janitor::clean_names(cpi)
cpi %>%
    select(ref_date,geo,products_and_product_groups,uom,value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y")) %>%
  filter(ref_date > from_this_year) %>% 
  filter(uom == "2002=100") %>%
  filter(geo %in% provinces) -> temp_CPI

  interactive_table(temp_CPI)
  temp_CPI %>%
  filter(products_and_product_groups %in% c("All-items", "Goods","Services")) %>%
  pivot_wider(names_from = products_and_product_groups, values_from = value) %>%
  select(!uom) -> clean_cpi

master_provincial_table <- left_join(master_provincial_table,clean_cpi) 
Joining, by = c("geo", "ref_date")

PPD

producer prices

Labour force

Labour force characteristics by industry,annual

LFS_annual <- cansim::get_cansim_ndm(annual_lfs_table_number)
Accessing CANSIM NDM product 14-10-0023 from Statistics Canada
Parsing data
Folding in metadata
LFS_annual <- janitor::clean_names(LFS_annual)
LFS_annual %>%
  select(everything()) %>%
  mutate(ref_date = parse_date(ref_date, "%Y")) %>%
  filter(ref_date > from_this_year) %>% 
  filter(age_group == "15 years and over") %>% 
  filter( sex == "Both sexes" ) %>%
  select(ref_date,geo,labour_force_characteristics,north_american_industry_classification_system_naics,value) %>% 
  pivot_wider(names_from = labour_force_characteristics, values_from = value) %>% interactive_table()

annual employment by indusury

agriculture/aquaculture

focusing on agric/aquaculture

grain shipmetns

grain_shipment <- cansim::get_cansim_ndm(grain_shipment_table_number)
Accessing CANSIM NDM product 32-10-0351 from Statistics Canada
Parsing data
Folding in metadata
grain_shipment <- janitor::clean_names(grain_shipment)
grain_shipment %>%
    select(ref_date,geo,type_of_grain,value) %>%
  mutate(ref_date = parse_date(ref_date, "%Y-%m")) %>%
  filter(ref_date > from_this_year) %>% 
  pivot_wider(names_from = type_of_grain, values_from = value) %>% 
  filter(geo %in% provinces) %>%
  as_tsibble(key = c(geo)) %>%
  group_by_key() %>%
  index_by(year_month = ~ year(.)) %>%
  summarise(across(where(is.numeric),list(sum))) %>%
  as_tibble() %>% 
  rename(ref_date = year_month) %>%
  mutate(ref_date = lubridate::make_date(ref_date)) %>% 
  select(everything()) -> master_annual_grain_shipment
Using `ref_date` as index variable.
master_provincial_table <- left_join(master_provincial_table,master_annual_grain_shipment)
Joining, by = c("geo", "ref_date")

results

interactive_table(master_provincial_table)

tips

maybe use * in regex to match wild cards with the coordinates; for example 1.1.** *

use this mutate_at(vars(births:residual_deviation), list(~ .x - lag(.x))) to automatically create a first difference of any table.

rename_with(iris, ~ tolower(gsub(“.”, "_", .x, fixed = TRUE)))

things I should have done

created a schema for naming things; I should have followed the statcan names instead of naming things for simplicity. create code templates with text generations based on three scenarios annual tables weekly tables monthly tables


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=English_Canada.1252  LC_CTYPE=English_Canada.1252   
[3] LC_MONETARY=English_Canada.1252 LC_NUMERIC=C                   
[5] LC_TIME=English_Canada.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.7.9.2  tsibble_0.9.3      summarytools_0.9.8 here_1.0.1        
 [5] cansim_0.3.5       janitor_2.0.1      DT_0.16            forcats_0.5.0     
 [9] stringr_1.4.0      dplyr_1.0.2        purrr_0.3.4        readr_1.4.0       
[13] tidyr_1.1.2        tibble_3.0.4       ggplot2_3.3.2      tidyverse_1.3.0   
[17] workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] httr_1.4.2         jsonlite_1.7.2     modelr_0.1.8       assertthat_0.2.1  
 [5] pander_0.6.3       cellranger_1.1.0   yaml_2.2.1         pillar_1.4.7      
 [9] backports_1.2.1    glue_1.4.2         digest_0.6.27      pryr_0.1.4        
[13] promises_1.1.1     checkmate_2.0.0    rvest_0.3.6        snakecase_0.11.0  
[17] colorspace_2.0-0   plyr_1.8.6         htmltools_0.5.0    httpuv_1.5.4      
[21] pkgconfig_2.0.3    broom_0.7.3        haven_2.3.1        magick_2.5.2      
[25] scales_1.1.1       whisker_0.4        later_1.1.0.1      git2r_0.27.1      
[29] generics_0.1.0     ellipsis_0.3.1     gapminder_0.3.0    withr_2.3.0       
[33] cli_2.2.0          magrittr_2.0.1     crayon_1.3.4       readxl_1.3.1      
[37] evaluate_0.14      ps_1.5.0           fs_1.5.0           fansi_0.4.1       
[41] anytime_0.3.9      xml2_1.3.2         rapportools_1.0    tools_4.0.3       
[45] hms_0.5.3          lifecycle_0.2.0    matrixStats_0.57.0 munsell_0.5.0     
[49] reprex_0.3.0       compiler_4.0.3     rlang_0.4.9        grid_4.0.3        
[53] rstudioapi_0.13    htmlwidgets_1.5.3  crosstalk_1.1.0.1  tcltk_4.0.3       
[57] base64enc_0.1-3    rmarkdown_2.6      codetools_0.2-18   gtable_0.3.0      
[61] DBI_1.1.0          curl_4.3           R6_2.5.0           knitr_1.30        
[65] utf8_1.1.4         rprojroot_2.0.2    stringi_1.5.3      Rcpp_1.0.5        
[69] vctrs_0.3.6        dbplyr_2.0.0       tidyselect_1.1.0   xfun_0.19